The embodiments of the present invention relate to wireless communication networks, more specifically, Multiple-Input-Multiple-Output (MIMO) communication networks. It is general understanding that a wireless stations refers to either a mobile terminal or a fixed terminal such as a base station.
A typical MIMO network is comprised of a base station (BS) with multiple antennas and multiple mobile stations (MS), at least one of which has multiple antennas. Assuming that there are M antennas at the BS and N antennas at one of the MSs, there are M×N MIMO channels between the BS and the MS. Let yi denote the signal received by antenna i on the MS, xj the signal transmitted by antenna j on the BS, and ni the channel noise received by antenna i on the MS.
The receiving signals at the MS are illustrated with the following equation: Y=H*x+n (1), where Y is a vector representing signals Y=(y1, . . . , yN) received by the N antennas on the MS, x is the vector representing signals x=(x1, . . . , xM) transmitted from M antennas on the BS, H is the M×N channel matrix between the BS and the MS, and n is a vector representing channel noise n=(n1, . . . , nN) received by N antennas on the MS.
The MIMO channel matrix H can be decomposed with the singular value decomposition (SVD) method
H=U*D*V+(2), where U and V are unitary matrices, operator (.)+ denotes conjugate transpose of the enclosed vector, and matrix D contains the channel singular values of H, which are the square roots of the eigenvalue of H*H+ and H+*H. The well-known terminologies, such as unitary matrix and conjugate transpose present in the disclosure, should be apparent to one having skills in the art and have not been described in details in order to avoid obscuring the disclosure.
By substituting H in equation (1) with equation (2), the equation (1) becomes Y=H*x+n=U*D*V+*x+n (3).
By multiplying equating (3) with U+, equation (3) is transformed into equation (4).
                                                                                                              U                    +                                    *                  Y                                =                                                      Y                    ~                                    =                                                                                    U                        +                                            *                      U                      *                      D                      *                                              V                        +                                            *                      x                                        +                                                                  U                        +                                            *                      n                                                                                                                                              =                                                      D                    *                                          V                      +                                        *                    x                                    +                                                            U                      +                                        *                    n                                                                                                                                            =                                                            D                      *                                              x                        ~                                                              +                                          n                      ~                                                                      ,                                                    ⁢                                  ⁢                                            where              ⁢                                                          ⁢                              Y                ~                                      =                                          U                +                            *              Y                                ,                                    x              ~                        =                                          V                +                            *              x                                ,                                    and              ⁢                                                          ⁢                              n                ~                                      =                                          U                +                            *                              n                .                                                                        (        4        )            
The operation described above creates L parallel channels, and the receiving signal {tilde over (y)} of each channel is described by {tilde over (y)}i=di*{tilde over (x)}i+ñi (5), where i=1, 2, . . . L, di is the channel fading coefficient, {tilde over (x)}i is the transmission signal, and ñi is the channel noise. L is the rank of channel matrix H of the MIMO network and L≦min(M, N).
Equation 5 shows that there exist L parallel channels (L≦min(M, N)) between the BS and the MS with each channel independently carrying one signal on the same frequency simultaneously. If the signals in the L channel are different from each other, there will be a L fold of increase in the channel capacity. If the signals in the all L channel are all the same, there will be L fold increase in the diversity.
One common practice of the MIMO network is to group the L channels into several subgroups. The same signal is sent over all channels within a subgroup but each subgroup carries a different signal. The practice provides space-time coding to increase diversity as well as spatial multiplexing to increase channel capacity.
The correlations among the signals received by the receiving antennas depend on the channel conditions and the degree of correlations determines the rank of channel matrix H. Channel matrix H, in turn, determines the performance of the MIMO network.
In an environment with severe multipath, the signals received by every antenna on the MS are highly uncorrelated. Consequently, MIMO channel matrix H has a high rank. In a good environment where the MS is in the Line-Of-Sight (LOS) range, the signals received by every antenna on the MS are highly correlated. As a result, MIMO channel matrix H has a lower rank. The lowest rank for channel matrix H is one, i.e., all signals received by different antennas are correlated. In this case, the MIMO network is degenerated into a conventional Single-Input-Single-Output (SISO) system.
The conventional MIMO network faces at least two challenges: the overhead, incurred in the link adaptation process, and the contradiction between the requirement for a channel matrix H with a higher rank and that for good signal to noise ratio (SNR) to have better performance.
The correlations among the signals received by the receiving antennas change with the environment. One of the functions of the MIMO network is to create L parallel MIMO channels in the link adaptation process after channel matrix H passes some predetermined tests, one of which measures how long the channel matrix H holds a rank L. In order to determine how long the channel matrix H holds a rank L, the MIMO network monitors the channel condition constantly and changes the MIMO network configuration accordingly. This results in the instability of the MIMO channels and incurs a lot of overhead in exchange for control signals.
SNR is one of the factors affecting the performance of the MIMO network. An environment with little propagation impairment where the MS is in the LOS range or there is little multipath or attenuation in the signal path, has better SNR. On the other hand, an environment with severe multipath offers a channel matrix with a higher rank. In reality, it is rare to have a situation in which an environment offers both good SNR and severe multipath. The method for beamformed MIMO network disclosed in the present invention provides a better solution for aforementioned issues.